Hành vi né tránh quảng cáo trên mạng xã hội: Phương pháp kết hợp hai giai đoạn giữa mô hình phương trình cấu trúc và mạng Nơ-Ron nhân tạo
0
Ngày xuất bản Online:
25/12/2025
Chuyên mục:
Quản trị kinh doanh, Marketing, Thương mại, Du lịch
Nội dung chính của bài viết
Tóm tắt
Nghiên cứu tìm hiểu các yếu tố tác động đến hành vi né tránh quảng cáo trên mạng xã hội (BAA) của người tiêu dùng tại Việt Nam. Dựa trên lý thuyết về động lực tiếp cận – né tránh, nghiên cứu này xem xét vai trò của nội dung quảng cáo không liên quan (AIR), tần suất quảng cáo (ADF), trải nghiệm tiêu cực với thương hiệu (NPE), hoài nghi về quảng cáo từ người ảnh hưởng (ASI), truyền miệng tiêu cực (NWOM), và lo ngại về quyền riêng tư (PC). Dữ liệu được thu thập từ 310 người trả lời hợp lệ thông qua bảng câu hỏi trực tuyến, và được phân tích bằng phương pháp hai giai đoạn kết hợp giữa mô hình PLS-SEM và mạng nơ-ron nhân tạo (ANN) để đánh giá các mối quan hệ tuyến tính và phi tuyến tính. Kết quả nghiên cứu cho thấy, AIR và ADF là hai yếu tố có ảnh hưởng mạnh mẽ và ổn định nhất trong cả hai phương pháp phân tích. Ngoài ra tầm quan trọng của NWOM cũng được cả hai phân tích này khẳng định. Trong khi NPE cho thấy, hiệu ứng mạnh hơn trong mô hình PLS-SE, ASI và PC thể hiện hiệu ứng tác động yếu nhưng vẫn có giá trị thống kê và cần thiết phải nghiên cứu thêm. Nghiên cứu này góp phần hiểu rõ hơn về khả năng né tránh quảng cáo trên mạng xã hội, khám phá ra những khác biệt tinh tế trong sức mạnh dự đoán của mô hình và gợi ý những chiến lược quan trọng cho nhà quản trị marketing trong thiết kế quảng cáo nhằm giảm thiểu tình trạng né tránh quảng cáo của người tiêu dùng.
Abstract
This study investigates the factors influencing consumers' behavior in Vietnam, such as avoiding advertising on social media (BAA). Based on the approach-avoidance motivation theory, the study examines the roles of ad irrelevance (AIR), ad frequency (ADF), negative past experience (NPE), ad skepticism toward influencers (ASI), negative word-of-mouth (NWOM), and privacy concern (PC). Data were collected from 310 valid respondents through an online questionnaire and analyzed using a two-stage method combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) to assess linear relationships and non-linear structures' predictive importance. The findings reveal that AIR and ADF are the two most influential and stable factors affecting BAA across both analytical approaches. In addition, the importance of NWOM is also confirmed by both analyses. While NPE shows a stronger effect in the PLS-SEM model, ASI and PC exhibit weak effects but remain statistically significant and warrant further investigation. This study contributes insight into the understanding of advertising avoidance on social media with the predictive power of the models. It suggests important strategic implications for marketing managers in designing advertisements that minimize avoidance by optimizing content relevance and frequency.
Từ khóa
ANN; Hành vi né tránh quảng cáo; Quảng cáo trên mạng xã hội; PLS-SEM; Mạng nơ-ron nhân tạo
Chi tiết bài viết
Lĩnh vực kinh tế (JEL Codes)
C45 - Neural Networks and Related Topics - Econometric and Statistical Methods: Special Topics, M31 - Marketing - M37 - Advertising - Marketing and Advertising
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Trích dẫn bài báo
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